Chapter 13: CISNET lung models: comparison of model assumptions and model structures.

Sophisticated modeling techniques can be powerful tools to help us understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Readers of journal articles are, however, rarely supplied with modeling details. Six modeling groups collaborated as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) to investigate the contribution of U.S. tobacco-control efforts toward reducing lung cancer deaths over the period 1975-2000. The six models included in this monograph were developed independently and use distinct, complementary approaches toward modeling the natural history of lung cancer. The models used the same data for inputs, and agreed on the design of the analysis and the outcome measures. This article highlights aspects of the models that are most relevant to similarities of or differences between the results. Structured comparisons can increase the transparency of these complex models.

Chapter 5: Actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation.

The smoking history generator (SHG) developed by the National Cancer Institute simulates individual life/smoking histories that serve as inputs for the Cancer Intervention and Surveillance Modeling Network (CISNET) lung cancer models. In this chapter, we review the SHG inputs, describe its outputs, and outline the methodology behind it. As an example, we use the SHG to simulate individual life histories for individuals born between 1890 and 1984 for each of the CISNET smoking scenarios and use those simulated histories to compute the corresponding smoking prevalence over the period 1975-2000.

An economic evaluation of atorvastatin for primary prevention of cardiovascular events in type 2 diabetes.

OBJECTIVE: The CARDS trial, a multicentre, randomized, controlled trial, found that atorvastatin 10 mg/day for patients with type 2 diabetes mellitus and normal low-density lipoprotein (LDL)-cholesterol significantly reduced cardiovascular (CV) events, including stroke. We estimated the cost effectiveness of atorvastatin as primary prevention against CV disease from the short-term and lifetime US payer perspectives. RESEARCH DESIGN AND METHODS: We constructed a decision analytic (Markov) model to evaluate long-term costs and outcomes for atorvastatin 10 mg/day versus no HMG-CoA reductase inhibitor (statin) therapy for patients with type 2 diabetes and no history of a CV event. CV event rates and survival were based on risk equations calibrated to CARDS and applied to a US type 2 diabetes population; the atorvastatin effect on CV events was based on hazard ratios from CARDS; direct medical care costs were based on US treatment patterns and published costs analyses of patients with diabetes. Costs were valued in $US, year 2005 values; costs and benefits were discounted at 3% per annum. RESULTS: Within the time horizon of the trial (5 years), the cost effectiveness of atorvastatin was $US137 276 per QALY. At 10 years, the incremental cost per QALY improved to $US3640 per QALY. At 25 years, overall costs were lower and QALYs higher in the atorvastatin arm. Costs of managing CV events were lower after 5 years for patients treated with atorvastatin. CONCLUSIONS: For patients with type 2 diabetes and one additional risk factor for CV disease, normal LDL-cholesterol and no history of a CV event, primary prevention with atorvastatin appears to be cost saving and improve outcomes over 25 years, although it is costly from a short-term US payer perspective. From both a medical and an economic viewpoint, primary prevention is desirable in this patient population.

Additional common inputs for analyzing impact of adjuvant therapy and mammography on U.S. mortality.

In estimating the impact of mammography and adjuvant treatment on U.S. breast cancer mortality rates, several parameters were common to all the Cancer Intervention and Surveillance Modeling Network (CISNET) models participating in the breast cancer base case. Models either used the parameters directly as input or calibrated their models to reproduce the common set of parameters. This chapter describes the common input parameters that are not specifically discussed elsewhere in the monograph.

Impact of adjuvant therapy and mammography on U.S. mortality from 1975 to 2000: comparison of mortality results from the cisnet breast cancer base case analysis.

The CISNET breast cancer program is a consortium of seven research groups modeling the impact of various cancer interventions on the national trends of breast cancer incidence and mortality. Each of the modeling groups participated in a CISNET breast cancer base case analysis with the objective of assessing the impact of mammography and adjuvant therapy on breast cancer mortality between 1975 and 2000. The comparative modeling approach used to address this question allowed for a unique view into the process of modeling. Results shown here expand on those recently reported in the New England Journal of Medicine (Berry et al., N Engl J Med 2005;353:1784-92) by presenting mortality impact in several different ways to facilitate comparisons between models. Comparisons of each group's results in the context of modeling assumptions made during the process gave insight into how specific model assumptions may have affected the results. The median estimate for the percent decline in breast cancer mortality due to mammography was 15% (range of 8%-23%), and the median estimate for the percent decline in mortality due to adjuvant treatment was 19% (range of 12%-21%). A detailed discussion of the differences in modeling approaches and how those differences may have influenced the mortality results concludes the chapter.

A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends.

The CISNET Breast Cancer program is a National Cancer Institute-sponsored collaboration composed of seven research groups that have modeled the impact of screening and adjuvant treatment on trends in breast cancer incidence and mortality over the period 1975-2000 (base case). This collaboration created a unique opportunity to make direct comparison of results from different models of population-based cancer screening produced in response to the same question. Comparing results in all but the most cursory way necessitates comparison of the models themselves. Previous chapters have discussed the models individual in detail. This chapter will aid the reader in understanding key areas of difference between the models. A focused analysis of differences and similarities between the models is presented with special attention paid to areas deemed most likely to contribute substantially to the results of the target analysis.